Chapter 4 Visualization
In this section we’ll explore visualization methods in R. Visualization has been a key element of R since its inception, since visualization is central to the exploratory philosophy of the language. The base plot system generally does a good job in coming up with the most likely graphical output based on the data you provide.
Figure 4.1: Flipper length by species
Figure 4.2: Flipper length by species
4.1 ggplot2
We’ll mostly focus however on gpplot2, based on the Grammar of Graphics because it provides considerable control over your graphics while remaining fairly easily readable, as long as you buy into its grammar.
ggplot2 looks at three aspects of a graph:
- data : where are the data coming from?
- geometry : what type of graph are we creating?
- aesthetics : what choices can we make about symbology and how do we connect symbology to data?
See https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
The ggplot2 system provides plots of single and multiple variables, using various coordinate systems (including geographic).
4.2 Plotting one variable
- continuous
- histograms
- density plots
- dot plots
- discrete
- bar
## DistNtoS elevation vegetation
## Min. : 0.0 Min. :1510 Length:29
## 1st Qu.: 37.0 1st Qu.:1510 Class :character
## Median :175.0 Median :1511 Mode :character
## Mean :164.7 Mean :1511
## 3rd Qu.:275.5 3rd Qu.:1511
## Max. :298.8 Max. :1511
## geometry NDVIgrowing NDVIsenescent
## Length:29 Min. :0.3255 Min. :0.1402
## Class :character 1st Qu.:0.5052 1st Qu.:0.2418
## Mode :character Median :0.6169 Median :0.2817
## Mean :0.5901 Mean :0.3662
## 3rd Qu.:0.6768 3rd Qu.:0.5407
## Max. :0.7683 Max. :0.7578
4.2.1 Histogram
First, to prepare the data, we need to use a pivot_longer on XSptsNDVI:
XSptsPheno <- XSptsNDVI %>%
filter(vegetation != "pine") %>%
pivot_longer(cols = starts_with("NDVI"), names_to = "phenology", values_to = "NDVI") %>%
mutate(phenology = str_sub(phenology, 5, str_length(phenology)))## Parsed with column specification:
## cols(
## DistNtoS = col_double(),
## elevation = col_double(),
## vegetation = col_character(),
## geometry = col_character(),
## phenology = col_character(),
## NDVI = col_double()
## )
Figure 4.3: Distribution of NDVI, Knuthson Meadow
Normal histogram: easier to visualize the distribution, see modes
## `stat_bin()` using `bins = 30`. Pick better value with
## `binwidth`.
Figure 4.4: Distribution of Average Monthly Temperatures, Sierra Nevada
Cumulative histogram with proportions: easier to see percentiles, median
n <- length(sierraData$TEMPERATURE)
sierraData %>%
ggplot(aes(TEMPERATURE)) +
geom_histogram(aes(y=cumsum(..count..)/n), fill="dark goldenrod")## `stat_bin()` using `bins = 30`. Pick better value with
## `binwidth`.
Figure 4.5: Cumulative Distribution of Average Monthly Temperatures, Sierra Nevada
4.2.2 Density Plot
Density represents how much out of the total. The total area (sum of widths of bins times densities of that bin) adds up to 1.
Figure 4.6: Density plot of NDVI, Knuthson Meadow
Note that NDVI values are <1 so bins are very small numbers, so in this case densities can be >1.
Using alpha and mapping phenology as fill color. This illustrates two useful ggplot methods:
- “mapping” a variable (phenology) to an aesthetic property (fill color of the density polygon)
- setting a a property (alpha = 0.2) to all polygons of the density plot. The alpha channel of colors defines its opacity, from invisible (0) to opaque (1) so is commonly used to set as its reverse, transparency.
Figure 4.7: Runoff under Eucalyptus and Oak in Bay Area sites
4.2.3 boxplot
Figure 4.8: Runoff under Eucalyptus and Oak, Bay Area Sites
Get color from tree within aes()
Figure 4.9: Runoff at Bay Area Sites, colored as Eucalyptus and Oak
Visualizing soil CO_2_ data with a Tukey box plot
co2 <- soilCO2_97
co2$SITE <- factor(co2$SITE) # in order to make the numeric field a factor
ggplot(data = co2, mapping = aes(x = SITE, y = `CO2%`)) +
geom_boxplot()
Figure 4.10: Visualizing soil CO_2_ data with a Tukey box plot
4.3 Plotting two variables
4.3.1 Two continuous variables
We’ve looked at this before – the scatterplot
Figure 4.11: Scatter plot of February temperature vs elevation
- The aes (“aesthetics”) function specifies the variables to use as x and y coordinates
- geom_point creates a scatter plot of those coordinate points
Set color for all (not in aes())
- color is defined outside of aes, so is applies to all points.
- mapping is first argument of geom_point, so
mapping =is not needed.
4.4 Color systems
You can find a lot about color systems. See these sources:
http://sape.inf.usi.ch/quick-reference/ggplot2/colour http://applied-r.com/rcolorbrewer-palettes/
4.4.1 Color from variable, in aesthetics
In this graph, color is defined inside aes, so is based on COUNTY
Figure 4.13: Color set within aes()
Plotting lines using the same x,y in aesthetics
sierraFeb %>%
ggplot(aes(TEMPERATURE,ELEVATION)) +
geom_point(color="blue") +
geom_line(color="red")
Figure 4.14: Using aesthetics settings for both points and lines
Note the use of pipe to start with the data then apply ggplot.
River map & profile
x <- c(1000, 1100, 1300, 1500, 1600, 1800, 1900)
y <- c(500, 700, 800, 1000, 1200, 1300, 1500)
z <- c(0, 1, 2, 5, 25, 75, 150)
d <- rep(NA, length(x))
longd <- rep(NA, length(x))
s <- rep(NA, length(x))
for(i in 1:length(x)){
if(i==1){longd[i] <- 0; d[i] <-0}
else{
d[i] <- sqrt((x[i]-x[i-1])^2 + (y[i]-y[i-1])^2)
longd[i] <- longd[i-1] + d[i]
s[i-1] <- (z[i]-z[i-1])/d[i]}}
longprofile <- bind_cols(x=x,y=y,z=z,d=d,longd=longd,s=s)
ggplot(longprofile, aes(x,y)) +
geom_line(mapping=aes(col=s), size=1.2) +
geom_point(mapping=aes(col=s, size=z)) +
coord_fixed(ratio=1) + scale_color_gradient(low="green", high="red") +
ggtitle("Simulated river path, elevations and slopes")
Figure 4.15: Longitudinal Profiles
ggplot(longprofile, aes(longd,z)) + geom_line(aes(col=s), size=1.5) + geom_point() +
scale_color_gradient(low="green", high="red") +
ggtitle("Elevation over longitudinal distance upstream")
Figure 4.16: Longitudinal Profiles
ggplot(longprofile, aes(longd,s)) + geom_point(aes(col=s), size=3) +
scale_color_gradient(low="green", high="red") +
ggtitle("Slope over longitudinal distance upstream")## Warning: Removed 1 rows containing missing values (geom_point).
Figure 4.17: Longitudinal Profiles
4.4.2 Trend line
sierraFeb %>%
ggplot(aes(TEMPERATURE,ELEVATION)) +
geom_point(color="blue") +
geom_smooth(color="red", method="lm")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values
## (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
Figure 4.18: Trend line using geom_smooth with a linear model
4.4.3 General symbology
A useful vignette accessed by vignette("ggplot2-specs") lets you see aesthetic specifications for symbols, including:
- Color & fill
- Lines
- line type, size, ends
- Polygon
- border color, linetype, size
- fill
- Points
- shape
- size
- color & fill
- stroke
- Text
- font face & size
- justification
4.4.3.1 Categorical symbology
One example of a “Big Data” resource is EPA’s Toxic Release Inventory that tracks releases from a wide array of sources, from oil refineries on down. One way of dealing with big data in terms of exploring meaning is to use symbology to try to make sense of it.
csvPath <- system.file("extdata","TRI_2017_CA.csv", package="iGIScData")
TRI <- read_csv(csvPath) %>%
filter(`5.1_FUGITIVE_AIR` > 100 & `5.2_STACK_AIR` > 100)## Warning: Missing column names filled in: 'X110' [110]## Warning: 3807 parsing failures.
## row col expected actual file
## 1 -- 110 columns 109 columns 'C:/Users/900008452/Documents/R/win-library/4.0/iGIScData/extdata/TRI_2017_CA.csv'
## 2 -- 110 columns 109 columns 'C:/Users/900008452/Documents/R/win-library/4.0/iGIScData/extdata/TRI_2017_CA.csv'
## 3 -- 110 columns 109 columns 'C:/Users/900008452/Documents/R/win-library/4.0/iGIScData/extdata/TRI_2017_CA.csv'
## 4 -- 110 columns 109 columns 'C:/Users/900008452/Documents/R/win-library/4.0/iGIScData/extdata/TRI_2017_CA.csv'
## 5 -- 110 columns 109 columns 'C:/Users/900008452/Documents/R/win-library/4.0/iGIScData/extdata/TRI_2017_CA.csv'
## ... ... ........... ........... ..................................................................................
## See problems(...) for more details.ggplot(data = TRI, aes(log(`5.2_STACK_AIR`), log(`5.1_FUGITIVE_AIR`),
color = INDUSTRY_SECTOR)) +
geom_point()
Figure 4.19: EPA Toxic Release Inventory, as a big data set needing symbology clarification
4.4.3.2 Graphs from grouped data
XSptsPheno %>%
ggplot() +
geom_point(aes(elevation, NDVI, shape=vegetation,
color = phenology), size = 3) +
geom_smooth(aes(elevation, NDVI,
color = phenology), method="lm") ## `geom_smooth()` using formula 'y ~ x'
Figure 4.20: NDVI symbolized by vegetation in two seasons
ggplot(data = tidy_eucoak) +
geom_point(mapping = aes(x = rain_mm, y = runoff_L, color = tree)) +
geom_smooth(mapping = aes(x = rain_mm, y= runoff_L, color = tree),
method = "lm") +
scale_color_manual(values = c("seagreen4", "orange3"))## `geom_smooth()` using formula 'y ~ x'
Figure 4.21: Eucalyptus and Oak: rainfall and runoff
4.4.3.3 Faceted graphs
This is another option to displaying groups of data, with parallel graphs
ggplot(data = tidy_eucoak) +
geom_point(aes(x=rain_mm,y=runoff_L)) +
geom_smooth(aes(x=rain_mm,y=runoff_L), method="lm") +
facet_grid(tree ~ .)## `geom_smooth()` using formula 'y ~ x'
Figure 4.22: Faceted graph alternative
4.5 Titles and subtitles
ggplot(data = tidy_eucoak) +
geom_point(aes(x=rain_mm,y=runoff_L, color=tree)) +
geom_smooth(aes(x=rain_mm,y=runoff_L, color=tree), method="lm") +
scale_color_manual(values=c("seagreen4","orange3")) +
labs(title="rainfall ~ runoff",
subtitle="eucalyptus & oak sites, 2016")## `geom_smooth()` using formula 'y ~ x'
Figure 4.23: Titles added
4.6 Pairs Plot
Figure 4.24: Pairs plot for Sierra Nevada stations variables
There are many versions of pairs plots. Here’s one from GGally (need to install.packages("GGally") first):
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## ```
4.7 plot: [1,1] [=>———————————–] 6% est: 0s
4.8 plot: [1,2] [====>——————————–] 12% est: 0s
4.9 plot: [1,3] [======>——————————] 19% est: 0s
4.10 plot: [1,4] [========>—————————-] 25% est: 0s
4.11 plot: [2,1] [===========>————————-] 31% est: 0s
4.12 plot: [2,2] [=============>———————–] 38% est: 0s
4.13 plot: [2,3] [===============>———————] 44% est: 0s
4.14 plot: [2,4] [=================>——————-] 50% est: 0s
4.15 plot: [3,1] [====================>—————-] 56% est: 0s
4.16 plot: [3,2] [======================>————–] 62% est: 0s
4.17 plot: [3,3] [========================>————] 69% est: 0s
4.18 plot: [3,4] [===========================>———] 75% est: 0s
4.19 plot: [4,1] [=============================>——-] 81% est: 0s
4.20 plot: [4,2] [===============================>—–] 88% est: 0s
4.21 plot: [4,3] [==================================>–] 94% est: 0s
4.22 plot: [4,4] [=====================================]100% est: 0s
<div class="figure">
<img src="EnvDataSci_files/figure-html/unnamed-chunk-101-1.png" alt="Pairs plot in GGally package" width="672" />
<p class="caption">(\#fig:unnamed-chunk-101)Pairs plot in GGally package</p>
</div>
## Exercises
1. Create a bar graph of the counts of the species in the penguins data frame. What can you say about what it shows?
2. Use bind_cols in dplyr to create a tibble from built-in vectors state.abb and state.region, then use ggplot with geom_bar to create a bar graph of the four regions.
3. Convert the built-in time series `treering` into a tibble `tr`using the `tibble()` functions with the single variable assigned as `treering = treering`, then create a histogram, using that tibble and variable for the `data` and `x` settings needed.
4. Create and display a new tibble `st` using `bind_cols` with `Name=state.name`, `Abb=state.abb`, `Region=state.region`, and a tibble created from `state.x77` with `as_tibble`.
5. From `st`, create a density plot from the variable `Frost` (number of days with frost for that state). Approximately what is the modal value?
6. From `st` create a a boxplot of `Area` by `Region`. Which region has the highest and which has the lowest median Area? Do the same for `Frost`.
7. From st, compare murder rate (y=Murder) to Frost (x) in a scatter plot, colored by Region.
8. Add a trend line (smooth) with method="lm" to your scatterplot, not colored by Region (but keep the points colored by Region). What can you say about what this graph is showing you?
9. Add a title to your graph.
10. Change your scatterplot to place labels using the Abb variable (still colored by Region) using `geom_label(aes(label=Abb, col=Region))`. Any observations about outliers?
<!--chapter:end:03-visualization.Rmd-->
# Spatial R
We'll explore the basics of simple features (sf) for building spatial datasets, then some common mapping methods, probably:
- ggplot2
- tmap
## Spatial Data
To work with spatial data requires extending R to deal with it using packages. Many have been developed, but the field is starting to mature using international open GIS standards.
*`sp`* (until recently, the dominant library of spatial tools)
- Includes functions for working with spatial data
- Includes `spplot` to create maps
- Also needs `rgdal` package for `readOGR` – reads spatial data frames.
*`sf`* (Simple Features)
- ISO 19125 standard for GIS geometries
- Also has functions for working with spatial data, but clearer to use.
- Doesn't need many additional packages, though you may still need `rgdal` installed for some tools you want to use.
- Replacing `sp` and `spplot` though you'll still find them in code. We'll give it a try...
- Works with ggplot2 and tmap for nice looking maps.
Cheat sheet: https://github.com/rstudio/cheatsheets/raw/master/sf.pdf
#### simple feature geometry sfg and simple feature column sfc
### Examples of simple geometry building in sf
sf functions have the pattern st_*
st means "space and time"
See Geocomputation with R at https://geocompr.robinlovelace.net/ or https://r-spatial.github.io/sf/
for more details, but here's an example of manual feature creation of sf geometries (sfg):
```r
library(tidyverse)
library(sf)library(sf)
eyes <- st_multipoint(rbind(c(1,5), c(3,5)))
nose <- st_point(c(2,4))
mouth <- st_linestring(rbind(c(1,3),c(3, 3)))
border <- st_polygon(list(rbind(c(0,5), c(1,2), c(2,1), c(3,2),
c(4,5), c(3,7), c(1,7), c(0,5))))
face <- st_sfc(eyes, nose, mouth, border) # sfc = sf column
plot(face)
Figure 4.25: Building simple geometries in sf
The face was a simple feature column (sfc) built from the list of sfgs. An sfc just has the one column, so is not quite like a shapefile.
- But it can have a coordinate referencing system CRS, and so can be mapped.
- Kind of like a shapefile with no other attributes than shape
4.22.1 Building a mappable sfc from scratch
CA_matrix <- rbind(c(-124,42),c(-120,42),c(-120,39),c(-114.5,35),
c(-114.1,34.3),c(-114.6,32.7),c(-117,32.5),c(-118.5,34),c(-120.5,34.5),
c(-122,36.5),c(-121.8,36.8),c(-122,37),c(-122.4,37.3),c(-122.5,37.8),
c(-123,38),c(-123.7,39),c(-124,40),c(-124.4,40.5),c(-124,41),c(-124,42))
NV_matrix <- rbind(c(-120,42),c(-114,42),c(-114,36),c(-114.5,36),
c(-114.5,35),c(-120,39),c(-120,42))
CA_list <- list(CA_matrix); NV_list <- list(NV_matrix)
CA_poly <- st_polygon(CA_list); NV_poly <- st_polygon(NV_list)
sfc_2states <- st_sfc(CA_poly,NV_poly,crs=4326) # crs=4326 specifies GCS
st_geometry_type(sfc_2states)## [1] POLYGON POLYGON
## 18 Levels: GEOMETRY POINT LINESTRING POLYGON ... TRIANGLE
Figure 4.26: A simple map built from scratch with hard-coded data as simple feature columns
sf class
Is like a shapefile: has attributes to which geometry is added, and can be used like a data frame.
attributes <- bind_rows(c(abb="CA", area=423970, pop=39.56e6),
c(abb="NV", area=286382, pop=3.03e6))
twostates <- st_sf(attributes, geometry = sfc_2states)
ggplot(twostates) + geom_sf() + geom_sf_text(aes(label = abb))## Warning in st_point_on_surface.sfc(sf::st_zm(x)):
## st_point_on_surface may not give correct results for longitude/
## latitude data
Figure 4.27: Using an sf class to build a map, displaying an attribute
4.22.2 Creating features from shapefiles or tables
sf’s st_read reads shapefiles
- shapefile is an open GIS format for points, polylines, polygons
You would normally have shapefiles (and all the files that go with them – .shx, etc.) stored on your computer, but we’ll access one from the iGIScData external data folder:
library(iGIScData)
library(sf)
shpPath <- system.file("extdata","CA_counties.shp", package="iGIScData")
CA_counties <- st_read(shpPath)## Reading layer `CA_counties' from data source `C:\Users\900008452\Documents\R\win-library\4.0\iGIScData\extdata\CA_counties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 58 features and 60 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.4152 ymin: 32.53427 xmax: -114.1312 ymax: 42.00952
## geographic CRS: WGS 84## Warning: plotting the first 9 out of 60 attributes; use max.plot =
## 60 to plot all
st_as_sf converts data frames
- using coordinates read from x and y variables, with crs set to coordinate system (4326 for GCS)
sierraFebpts <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs=4326)
plot(sierraFebpts)
library(tidyverse)
library(sf)
library(iGIScData)
censusCentroids <- st_centroid(BayAreaTracts)
TRI_sp <- st_as_sf(TRI_2017_CA, coords = c("LONGITUDE", "LATITUDE"),
crs=4326) # simple way to specify coordinate reference
bnd <- st_bbox(censusCentroids)
ggplot() +
geom_sf(data = BayAreaCounties, aes(fill = NAME)) +
geom_sf(data = censusCentroids) +
geom_sf(data = CAfreeways, color = "grey") +
geom_sf(data = TRI_sp, color = "yellow") +
coord_sf(xlim = c(bnd[1], bnd[3]), ylim = c(bnd[2], bnd[4])) +
labs(title="Bay Area Counties, Freeways and Census Tract Centroids")
Figure 4.28: ggplot map of Bay Area TRI sites, census centroids, freeways
4.22.3 Coordinate Referencing System
Say you have data you need to make spatial with a spatial reference
sierra <- read_csv("sierraClimate.csv")
EPSG or CRS codes are an easy way to provide coordinate referencing.
Two ways of doing the same thing.
- Spell it out:
GCS <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
wsta = st_as_sf(sierra, coords = c("LONGITUDE","LATITUDE"), crs=GCS)- Google to find the code you need and assign it to the crs parameter:
wsta <- st_as_sf(sierra, coords = c("LONGITUDE","LATITUDE"), crs=4326)
4.22.3.1 Removing Geometry
There are many instances where you want to remove geometry from a sf data frame
Some R functions run into problems with geometry and produce confusing error messages, like “non-numeric argument”
You’re wanting to work with an sf data frame in a non-spatial way
One way to remove geometry:
myNonSFdf <- mySFdf %>% st_set_geometry(NULL)
4.22.4 Spatial join st_join
A spatial join with st_join joins data from census where TRI points occur
4.22.5 Plotting maps in the base plot system
There are various programs for creating maps from spatial data, and we’ll look at a few after we’ve looked at rasters. As usual, the base plot system often does something useful when you give it data.
## Warning: plotting the first 9 out of 60 attributes; use max.plot =
## 60 to plot all
And with just one variable:
There’s a lot more we could do with the base plot system, but we’ll mostly focus on some better options in ggplot2 and tmap.
4.23 Raster GIS in R
Simple Features are feature-based, so based on the name I guess it’s not surprising that sf doesn’t have support for rasters. But we can use the raster package for that.
A bit of raster reading and map algebra with Marble Mountains elevation data
library(raster)
rasPath <- system.file("extdata","elev.tif", package="iGIScData")
elev <- raster(rasPath)
slope <- terrain(elev, opt="slope")
aspect <- terrain(elev, opt="aspect")
slopeclasses <-matrix(c(0,0.2,1, 0.2,0.4,2, 0.4,0.6,3,
0.6,0.8,4, 0.8,1,5), ncol=3, byrow=TRUE)
slopeclass <- reclassify(slope, rcl = slopeclasses)
plot(elev)
4.24 ggplot2 for maps
The Grammar of Graphics is the gg of ggplot.
- Key concept is separating aesthetics from data
- Aesthetics can come from variables (using aes()setting) or be constant for the graph
Mapping tools that follow this lead
- ggplot, as we have seen, and it continues to be enhanced
- tmap (Thematic Maps) https://github.com/mtennekes/tmap Tennekes, M., 2018, tmap: Thematic Maps in R, Journal of Statistical Software 84(6), 1-39
Try ?geom_sf and you’ll find that its first parameters is mapping with aes() by default. The data property is inherited from the ggplot call, but commonly you’ll want to specify data=something in your geom_sf call.
Another simple ggplot, with labels
## Warning in st_point_on_surface.sfc(sf::st_zm(x)):
## st_point_on_surface may not give correct results for longitude/
## latitude data
and now with fill color
ggplot(CA_counties) + geom_sf(aes(fill = MED_AGE)) +
geom_sf_text(aes(label = NAME), col="white", size=1.5)## Warning in st_point_on_surface.sfc(sf::st_zm(x)):
## st_point_on_surface may not give correct results for longitude/
## latitude data
Repositioned legend, no “x” or “y” labels
ggplot(CA_counties) + geom_sf(aes(fill=MED_AGE)) +
geom_sf_text(aes(label = NAME), col="white", size=1.5) +
theme(legend.position = c(0.8, 0.8)) +
labs(x="",y="")
Map in ggplot2, zoomed into two counties:
library(tidyverse); library(sf); library(iGIScData)
census <- BayAreaTracts %>%
filter(CNTY_FIPS %in% c("013", "095"))
TRI <- TRI_2017_CA %>%
st_as_sf(coords = c("LONGITUDE", "LATITUDE"), crs=4326) %>%
st_join(census) %>%
filter(CNTY_FIPS %in% c("013", "095"),
(`5.1_FUGITIVE_AIR` + `5.2_STACK_AIR`) > 0)## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planarbnd = st_bbox(census)
ggplot() +
geom_sf(data = BayAreaCounties, aes(fill = NAME)) +
geom_sf(data = census, color="grey40", fill = NA) +
geom_sf(data = TRI) +
coord_sf(xlim = c(bnd[1], bnd[3]), ylim = c(bnd[2], bnd[4])) +
labs(title="Census Tracts and TRI air-release sites") +
theme(legend.position = "none")
4.24.1 Rasters in ggplot2
Raster display in ggplot2 is currently a little awkward, as are rasters in general in the feature-dominated GIS world.
We can use a trick: converting rasters to a grid of points:
library(tidyverse)
library(sf)
library(raster)
rasPath <- system.file("extdata","elev.tif", package="iGIScData")
elev <- raster(rasPath)
shpPath <- system.file("extdata","trails.shp", package="iGIScData")
trails <- st_read(shpPath)## Reading layer `trails' from data source `C:\Users\900008452\Documents\R\win-library\4.0\iGIScData\extdata\trails.shp' using driver `ESRI Shapefile'
## Simple feature collection with 32 features and 8 fields
## geometry type: LINESTRING
## dimension: XY
## bbox: xmin: 481903.8 ymin: 4599196 xmax: 486901.9 ymax: 4603200
## projected CRS: NAD83 / UTM zone 10Nelevpts = as.data.frame(rasterToPoints(elev))
ggplot() +
geom_raster(data = elevpts, aes(x = x, y = y, fill = elev)) +
geom_sf(data = trails)
4.25 tmap
Basic building block is tm_shape(data) followed by various layer elements such as tm_fill() shape can be features or raster See Geocomputation with R Chapter 8 “Making Maps with R” for more information. https://geocompr.robinlovelace.net/adv-map.html
##
## Attaching package: 'spData'## The following object is masked _by_ '.GlobalEnv':
##
## elev
Color by variable
tmap of sierraFeb with hillshade and point symbols
## tmap mode set to plottingtmap_options(max.categories = 8)
#sierraFeb <- st_read("data/sierraFeb.csv")
sierra <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)
#hillsh <- raster("data/ca_hillsh_WGS84.tif")
bounds <- st_bbox(sierra)
tm_shape(CAhillsh,bbox=bounds)+
tm_raster(palette="-Greys",legend.show=FALSE,n=10) + tm_shape(sierra) + tm_symbols(col="TEMPERATURE",
palette=c("blue","red"), style="cont",n=8) +
tm_legend() +
tm_layout(legend.position=c("RIGHT","TOP"))## stars object downsampled to 1092 by 915 cells. See tm_shape manual (argument raster.downsample)
Note: “-Greys” needed to avoid negative image, since “Greys” go from light to dark, and to match reflectance as with b&w photography, they need to go from dark to light.
4.25.1 Interactive Maps
The word “static” in “static maps” isn’t something you would have heard in a cartography class 30 years ago, since essentially all maps then were static. Very important in designing maps is considering your audience, and one characteristic of the audience of those maps of yore were that they were printed and thus fixed on paper. A lot of cartographic design relates to that property:
- Figure-to-ground relationships assume “ground” is a white piece of paper (or possibly a standard white background in a pdf), so good cartographic color schemes tend to range from light for low values to dark for high values.
- Scale is fixed and there are no “tools” for changing scale, so a lot of attention must be paid to providing scale information.
- Similarly, without the ability to see the map at different scales, inset maps are often needed to provide context.
Interactive maps change the game in having tools for changing scale, and always being “printed” on a computer or device where the color of the background isn’t necessarily white. We are increasingly used to using interactive maps on our phones or other devices, and often get frustrated not being able to zoom into a static map.
A widely used interactive mapping system is Leaflet, but we’re going to use tmap to access Leaflet behind the scenes and allow us to create maps with one set of commands. The key parameter needed is tmap_mode which must be set to “view” to create an interactive map.
## tmap mode set to interactive viewing